Improving and extending biodiversity indices

Lead Research Organisation: University of East Anglia
Department Name: Biological Sciences

Abstract

This project will develop a widely applicable, objective framework for indicator species selection that ensures quality, functionality and transparency and which can be used both to develop new indicator sets and critically review existing indicators to define the bounds of their functionality.

OBJECTIVE 1: QUANTIFY THE IMPACT OF ALTERNATIVE SPECIES-SELECTION PROTOCOLS ON INDICATOR REPRESENTATIVENESS, REACTIVITY AND PRECISION
Using simulated communities, varying in size and the relative proportion of generalist and specialist species, the student will critically assess the implications for indicator composition and characteristics of i) selecting species on the basis of comparative morphological, behavioural and/or reproductive traits and ii) imposing minimum range and trait usage thresholds for species' inclusion. Emergent patterns will be validated using Pan-European Common Bird Monitoring Scheme (PECBMS) data to deliver a robust, generally applicable protocol to select species for population-trend based indicators. This will be applied to European forest, farmland, wetland and montane systems, generating a stable of national and supranational indices that meet stakeholder requirements for extended coverage and benchmarking through standardised methodology.

OBJECTIVE 2: COMPARE AND CONTRAST THE TEMPORAL AND SPATIAL DYNAMICS OF INDICATORS REFLECTING ALTERNATIVE COMMUNITY CHARACTERISTICS
Current multi-species indicators are selected predominantly from a taxonomic perspective, providing a limited impression of community structure and dynamics. Demand for indicators of additional characteristics has grown, particularly with regard the assessment of ecosystem service delivery and natural capital stocks. Using the trait matrices and species sets generated in Obj 1, the student will develop and generate novel national and supranational indices of functional diversity, richness and evenness. The characteristics of these multi-faceted metrics will be compared to indices of population change to explore synergies and conflicts in the interpretation of biodiversity health they provide.

OBJECTIVE 3: DEVELOP NATIONAL AND SUPRANATIONAL INDICATORS FOR FARMLAND BIODIVERSITY
Extending existing and building new trait datasets, the student will generate new indicator sets on the basis of morphological, reproductive and/or behavioural traits (as Obj 1) and functional diversity (Obj 2) for pollinators, butterflies and mammals associated with European farmland systems. These sets will be compared and contrasted to make recommendations for targeted annual monitoring programmes. Index values based on long-term population trends will also be used to examine the temporal concordance in the dynamics of these indicators and assess the functionality and scope of wild bird indices as measures of wider biodiversity health.

The research proposed is set within a context of land-use and environmental change, global biodiversity loss and sustainable development and the student will be exposed to and challenged by some of the key research areas in ecology and conservation. S/he will receive training in the construction, handling and analyses of large, long-term monitoring databases, GIS and advanced spatial analyses in R and will develop a high level of competency in statistical modelling. There will also be a strong emphasis on participatory engagement in dissemination activities to both national and European stakeholders and academic communities. As such, the project will provide advanced, individually-tailored training in several of the key skills identified in NERC's Most Wanted II report.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
NE/R00742X/1 01/10/2017 30/09/2022
2087992 Studentship NE/R00742X/1 01/10/2018 31/03/2022 Enya O'Reilly
 
Description So far we have identified a method for classifying species by their association to and specialisation for a range of habitats using a continuous metric which is derived from species count data at site level. This is an improvement on categorical classifications for the number of habitats a species is associated with which is based on literature accounts or expert opinion and fails to identify the extent to which a species is associated with each habitat.
Exploitation Route Our outcomes so far can be used within research to more accurately identify species as strongly associated with one habitat or multiple habitats, thereby identifying it as a habitat specialist or generalist without relying on potential biased opinions on this categorisation. This would allow research to explore the impacts of anthropogenic activities on species based on their specialisation to a habitat of interest, and therefore better inform policy-makers of land management decisions that will better support species vulnerable to habitat degradation.
Sectors Environment